{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "581dc6c28537b6ed",
   "metadata": {},
   "source": [
    "# What kind of data does pandas handle?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-29T14:46:21.351387Z",
     "start_time": "2024-09-29T14:46:17.089359Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>22</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>35</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>58</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Name  Age     Sex\n",
       "0   Braund, Mr. Owen Harris   22    male\n",
       "1  Allen, Mr. William Henry   35    male\n",
       "2  Bonnell, Miss. Elizabeth   58  female"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(\n",
    "    {\n",
    "        \"Name\": [\n",
    "            \"Braund, Mr. Owen Harris\",\n",
    "            \"Allen, Mr. William Henry\",\n",
    "            \"Bonnell, Miss. Elizabeth\",\n",
    "        ],\n",
    "        \"Age\": [22, 35, 58],\n",
    "        \"Sex\": [\"male\", \"male\", \"female\"],\n",
    "    }\n",
    ")\n",
    "\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "221617ea928374aa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-29T14:49:37.963030Z",
     "start_time": "2024-09-29T14:49:37.958775Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Age\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d2cda855c2ab9bb6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-29T14:50:38.945243Z",
     "start_time": "2024-09-29T14:50:38.936672Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>18.230012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>46.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>58.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Age\n",
       "count   3.000000\n",
       "mean   38.333333\n",
       "std    18.230012\n",
       "min    22.000000\n",
       "25%    28.500000\n",
       "50%    35.000000\n",
       "75%    46.500000\n",
       "max    58.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b602a73dd333af4",
   "metadata": {},
   "source": [
    "# How do I read and write tabular data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "50934c81818f34fb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-29T14:57:04.583630Z",
     "start_time": "2024-09-29T14:57:04.577578Z"
    }
   },
   "outputs": [],
   "source": [
    "titanic = pd.read_csv('./data/titanic.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "244a78f1688e28fb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-29T14:57:07.764663Z",
     "start_time": "2024-09-29T14:57:07.757908Z"
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
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       "      <td>C</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>35.0</td>\n",
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       "      <td>S</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
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       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
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       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
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       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2660aedb68b74ccd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-29T14:57:37.777446Z",
     "start_time": "2024-09-29T14:57:37.771467Z"
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   },
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Master. Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "      <td>female</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>345763</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  \\\n",
       "0             1         0       3   \n",
       "1             2         1       1   \n",
       "2             3         1       3   \n",
       "3             4         1       1   \n",
       "4             5         0       3   \n",
       "5             6         0       3   \n",
       "6             7         0       1   \n",
       "7             8         0       3   \n",
       "8             9         1       3   \n",
       "9            10         1       2   \n",
       "10           11         1       3   \n",
       "11           12         1       1   \n",
       "12           13         0       3   \n",
       "13           14         0       3   \n",
       "14           15         0       3   \n",
       "15           16         1       2   \n",
       "16           17         0       3   \n",
       "17           18         1       2   \n",
       "18           19         0       3   \n",
       "19           20         1       3   \n",
       "\n",
       "                                                 Name     Sex   Age  SibSp  \\\n",
       "0                             Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1   Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                              Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3        Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                            Allen, Mr. William Henry    male  35.0      0   \n",
       "5                                    Moran, Mr. James    male   NaN      0   \n",
       "6                             McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                      Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8   Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                 Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "10                    Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n",
       "11                           Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "12                     Saundercock, Mr. William Henry    male  20.0      0   \n",
       "13                        Andersson, Mr. Anders Johan    male  39.0      1   \n",
       "14               Vestrom, Miss. Hulda Amanda Adolfina  female  14.0      0   \n",
       "15                   Hewlett, Mrs. (Mary D Kingcome)   female  55.0      0   \n",
       "16                               Rice, Master. Eugene    male   2.0      4   \n",
       "17                       Williams, Mr. Charles Eugene    male   NaN      0   \n",
       "18  Vander Planke, Mrs. Julius (Emelia Maria Vande...  female  31.0      1   \n",
       "19                            Masselmani, Mrs. Fatima  female   NaN      0   \n",
       "\n",
       "    Parch            Ticket     Fare Cabin Embarked  \n",
       "0       0         A/5 21171   7.2500   NaN        S  \n",
       "1       0          PC 17599  71.2833   C85        C  \n",
       "2       0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3       0            113803  53.1000  C123        S  \n",
       "4       0            373450   8.0500   NaN        S  \n",
       "5       0            330877   8.4583   NaN        Q  \n",
       "6       0             17463  51.8625   E46        S  \n",
       "7       1            349909  21.0750   NaN        S  \n",
       "8       2            347742  11.1333   NaN        S  \n",
       "9       0            237736  30.0708   NaN        C  \n",
       "10      1           PP 9549  16.7000    G6        S  \n",
       "11      0            113783  26.5500  C103        S  \n",
       "12      0         A/5. 2151   8.0500   NaN        S  \n",
       "13      5            347082  31.2750   NaN        S  \n",
       "14      0            350406   7.8542   NaN        S  \n",
       "15      0            248706  16.0000   NaN        S  \n",
       "16      1            382652  29.1250   NaN        Q  \n",
       "17      0            244373  13.0000   NaN        S  \n",
       "18      0            345763  18.0000   NaN        S  \n",
       "19      0              2649   7.2250   NaN        C  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8b8c6705647f5329",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-29T14:58:38.705994Z",
     "start_time": "2024-09-29T14:58:38.697421Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>882</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Markun, Mr. Johann</td>\n",
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       "      <td>33.0</td>\n",
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       "      <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7552</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>884</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Banfield, Mr. Frederick James</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A./SOTON 34068</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>885</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sutehall, Mr. Henry Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392076</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
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       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "881          882         0       3                        Markun, Mr. Johann   \n",
       "882          883         0       3              Dahlberg, Miss. Gerda Ulrika   \n",
       "883          884         0       2             Banfield, Mr. Frederick James   \n",
       "884          885         0       3                    Sutehall, Mr. Henry Jr   \n",
       "885          886         0       3      Rice, Mrs. William (Margaret Norton)   \n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "881    male  33.0      0      0            349257   7.8958   NaN        S  \n",
       "882  female  22.0      0      0              7552  10.5167   NaN        S  \n",
       "883    male  28.0      0      0  C.A./SOTON 34068  10.5000   NaN        S  \n",
       "884    male  25.0      0      0   SOTON/OQ 392076   7.0500   NaN        S  \n",
       "885  female  39.0      0      5            382652  29.1250   NaN        Q  \n",
       "886    male  27.0      0      0            211536  13.0000   NaN        S  \n",
       "887  female  19.0      0      0            112053  30.0000   B42        S  \n",
       "888  female   NaN      1      2        W./C. 6607  23.4500   NaN        S  \n",
       "889    male  26.0      0      0            111369  30.0000  C148        C  \n",
       "890    male  32.0      0      0            370376   7.7500   NaN        Q  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1a92ff65acac85e8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-29T14:59:03.100535Z",
     "start_time": "2024-09-29T14:59:03.095354Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "df29078de18eea9a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-29T15:01:00.962667Z",
     "start_time": "2024-09-29T15:01:00.831660Z"
    }
   },
   "outputs": [],
   "source": [
    "In [6]: titanic.to_excel(\"./data/titanic.xlsx\", sheet_name=\"passengers\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f6d2174a9488116",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-29T15:03:07.552753Z",
     "start_time": "2024-09-29T15:03:07.480062Z"
    }
   },
   "outputs": [],
   "source": [
    "In [7]: titanic = pd.read_excel(\"./data/titanic.xlsx\", sheet_name=\"passengers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "91586a4701bccdea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-29T15:04:10.672859Z",
     "start_time": "2024-09-29T15:04:10.666304Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic.info()"
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# How do I select a subset of a DataFrame",
   "id": "82cc6d2121c01aa5"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-01T14:49:58.605212Z",
     "start_time": "2024-10-01T14:49:58.058592Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "\n",
    "titanic = pd.read_csv(\"data/titanic.csv\")\n",
    "\n",
    "titanic.head()"
   ],
   "id": "6a744052d1ef0353",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-01T14:50:23.971856Z",
     "start_time": "2024-10-01T14:50:23.967834Z"
    }
   },
   "cell_type": "code",
   "source": [
    "ages = titanic[\"Age\"]\n",
    "\n",
    "ages.head()"
   ],
   "id": "17534e74ab0f850",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22.0\n",
       "1    38.0\n",
       "2    26.0\n",
       "3    35.0\n",
       "4    35.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-01T14:50:59.469528Z",
     "start_time": "2024-10-01T14:50:59.466436Z"
    }
   },
   "cell_type": "code",
   "source": "type(titanic[\"Age\"])",
   "id": "5f0c4c28dbb880a8",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-01T14:51:13.062010Z",
     "start_time": "2024-10-01T14:51:13.059087Z"
    }
   },
   "cell_type": "code",
   "source": "titanic[\"Age\"].shape",
   "id": "16fe431ad0adf9da",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891,)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-01T14:53:32.977226Z",
     "start_time": "2024-10-01T14:53:32.974825Z"
    }
   },
   "cell_type": "code",
   "source": "above_35 = titanic[titanic[\"Age\"] > 35]",
   "id": "d26df628fbc61e0f",
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-01T14:53:38.583474Z",
     "start_time": "2024-10-01T14:53:38.578395Z"
    }
   },
   "cell_type": "code",
   "source": "above_35",
   "id": "d942a74d214a2a23",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "1              2         1       1   \n",
       "6              7         0       1   \n",
       "11            12         1       1   \n",
       "13            14         0       3   \n",
       "15            16         1       2   \n",
       "..           ...       ...     ...   \n",
       "865          866         1       2   \n",
       "871          872         1       1   \n",
       "873          874         0       3   \n",
       "879          880         1       1   \n",
       "885          886         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "6                              McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "11                            Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "13                         Andersson, Mr. Anders Johan    male  39.0      1   \n",
       "15                    Hewlett, Mrs. (Mary D Kingcome)   female  55.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "865                           Bystrom, Mrs. (Karolina)  female  42.0      0   \n",
       "871   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)  female  47.0      1   \n",
       "873                        Vander Cruyssen, Mr. Victor    male  47.0      0   \n",
       "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female  56.0      0   \n",
       "885               Rice, Mrs. William (Margaret Norton)  female  39.0      0   \n",
       "\n",
       "     Parch    Ticket     Fare Cabin Embarked  \n",
       "1        0  PC 17599  71.2833   C85        C  \n",
       "6        0     17463  51.8625   E46        S  \n",
       "11       0    113783  26.5500  C103        S  \n",
       "13       5    347082  31.2750   NaN        S  \n",
       "15       0    248706  16.0000   NaN        S  \n",
       "..     ...       ...      ...   ...      ...  \n",
       "865      0    236852  13.0000   NaN        S  \n",
       "871      1     11751  52.5542   D35        S  \n",
       "873      0    345765   9.0000   NaN        S  \n",
       "879      1     11767  83.1583   C50        C  \n",
       "885      5    382652  29.1250   NaN        Q  \n",
       "\n",
       "[217 rows x 12 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
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       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
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       "      <td>2</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>866</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Bystrom, Mrs. (Karolina)</td>\n",
       "      <td>female</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>236852</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>872</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11751</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>D35</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>874</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Cruyssen, Mr. Victor</td>\n",
       "      <td>male</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345765</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>880</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
       "      <td>female</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11767</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>C50</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>217 rows × 12 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-01T14:54:12.786673Z",
     "start_time": "2024-10-01T14:54:12.783407Z"
    }
   },
   "cell_type": "code",
   "source": "titanic[\"Age\"] > 35",
   "id": "11cb4e672f19279e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1       True\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "       ...  \n",
       "886    False\n",
       "887    False\n",
       "888    False\n",
       "889    False\n",
       "890    False\n",
       "Name: Age, Length: 891, dtype: bool"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-01T15:00:12.772369Z",
     "start_time": "2024-10-01T15:00:12.767899Z"
    }
   },
   "cell_type": "code",
   "source": [
    "adult_names = titanic.loc[titanic[\"Age\"] > 35, \"Name\"]\n",
    "\n",
    "adult_names.head()"
   ],
   "id": "52523fb4a80ff3e6",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
       "6                               McCarthy, Mr. Timothy J\n",
       "11                             Bonnell, Miss. Elizabeth\n",
       "13                          Andersson, Mr. Anders Johan\n",
       "15                     Hewlett, Mrs. (Mary D Kingcome) \n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-01T15:04:32.075866Z",
     "start_time": "2024-10-01T15:04:32.070314Z"
    }
   },
   "cell_type": "code",
   "source": "titanic.iloc[9:25, 2:5]",
   "id": "8596d721756cb96",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    Pclass                                               Name     Sex\n",
       "9        2                Nasser, Mrs. Nicholas (Adele Achem)  female\n",
       "10       3                    Sandstrom, Miss. Marguerite Rut  female\n",
       "11       1                           Bonnell, Miss. Elizabeth  female\n",
       "12       3                     Saundercock, Mr. William Henry    male\n",
       "13       3                        Andersson, Mr. Anders Johan    male\n",
       "14       3               Vestrom, Miss. Hulda Amanda Adolfina  female\n",
       "15       2                   Hewlett, Mrs. (Mary D Kingcome)   female\n",
       "16       3                               Rice, Master. Eugene    male\n",
       "17       2                       Williams, Mr. Charles Eugene    male\n",
       "18       3  Vander Planke, Mrs. Julius (Emelia Maria Vande...  female\n",
       "19       3                            Masselmani, Mrs. Fatima  female\n",
       "20       2                               Fynney, Mr. Joseph J    male\n",
       "21       2                              Beesley, Mr. Lawrence    male\n",
       "22       3                        McGowan, Miss. Anna \"Annie\"  female\n",
       "23       1                       Sloper, Mr. William Thompson    male\n",
       "24       3                      Palsson, Miss. Torborg Danira  female"
      ],
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Master. Eugene</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3</td>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2</td>\n",
       "      <td>Fynney, Mr. Joseph J</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2</td>\n",
       "      <td>Beesley, Mr. Lawrence</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3</td>\n",
       "      <td>McGowan, Miss. Anna \"Annie\"</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>Sloper, Mr. William Thompson</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Miss. Torborg Danira</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# How do I create plots in pandas",
   "id": "358c79106ec34155"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-01T15:23:32.811357Z",
     "start_time": "2024-10-01T15:23:22.537699Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "air_quality = pd.read_csv(\"data/air_quality_no2.csv\", index_col=0, parse_dates=True)\n",
    "\n",
    "air_quality.head()"
   ],
   "id": "9d9341cd670cc318",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Matplotlib is building the font cache; this may take a moment.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "                     station_antwerp  station_paris  station_london\n",
       "datetime                                                           \n",
       "2019-05-07 02:00:00              NaN            NaN            23.0\n",
       "2019-05-07 03:00:00             50.5           25.0            19.0\n",
       "2019-05-07 04:00:00             45.0           27.7            19.0\n",
       "2019-05-07 05:00:00              NaN           50.4            16.0\n",
       "2019-05-07 06:00:00              NaN           61.9             NaN"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station_antwerp</th>\n",
       "      <th>station_paris</th>\n",
       "      <th>station_london</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-07 02:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 03:00:00</th>\n",
       "      <td>50.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 04:00:00</th>\n",
       "      <td>45.0</td>\n",
       "      <td>27.7</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 05:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>50.4</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 06:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>61.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-01T15:24:56.349165Z",
     "start_time": "2024-10-01T15:24:56.205427Z"
    }
   },
   "cell_type": "code",
   "source": "air_quality.plot()",
   "id": "f1ce221395c5e77f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='datetime'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-01T15:25:50.833930Z",
     "start_time": "2024-10-01T15:25:50.762846Z"
    }
   },
   "cell_type": "code",
   "source": "air_quality[\"station_paris\"].plot()",
   "id": "11af97b9a9dec9c1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='datetime'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-01T15:26:57.840590Z",
     "start_time": "2024-10-01T15:26:57.777944Z"
    }
   },
   "cell_type": "code",
   "source": "air_quality.plot.scatter(x=\"station_london\", y=\"station_paris\", alpha=0.5)",
   "id": "7a1403ca052a1c4",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='station_london', ylabel='station_paris'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-01T15:27:41.961712Z",
     "start_time": "2024-10-01T15:27:41.958344Z"
    }
   },
   "cell_type": "code",
   "source": [
    "[\n",
    "    method_name\n",
    "    for method_name in dir(air_quality.plot)\n",
    "    if not method_name.startswith(\"_\")\n",
    "]"
   ],
   "id": "7b7cfae7b7853d16",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['area',\n",
       " 'bar',\n",
       " 'barh',\n",
       " 'box',\n",
       " 'density',\n",
       " 'hexbin',\n",
       " 'hist',\n",
       " 'kde',\n",
       " 'line',\n",
       " 'pie',\n",
       " 'scatter']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-01T15:28:54.484320Z",
     "start_time": "2024-10-01T15:28:54.409458Z"
    }
   },
   "cell_type": "code",
   "source": "air_quality.plot.box()",
   "id": "8d6cfa5b214c4a74",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-01T15:29:38.399197Z",
     "start_time": "2024-10-01T15:29:38.167785Z"
    }
   },
   "cell_type": "code",
   "source": "axs = air_quality.plot.area(figsize=(12, 4), subplots=True)",
   "id": "6807a310d1e5e5f6",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-01T15:31:54.355192Z",
     "start_time": "2024-10-01T15:31:54.196093Z"
    }
   },
   "cell_type": "code",
   "source": [
    "fig, axs = plt.subplots(figsize=(12, 4))\n",
    "\n",
    "air_quality.plot.area(ax=axs)\n",
    "\n",
    "axs.set_ylabel(\"NO$_2$ concentration\")\n",
    "\n",
    "fig.savefig(\"result/no2_concentrations.png\")\n",
    "\n",
    "plt.show()"
   ],
   "id": "56852c3054f26de1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# How to create new columns derived from existing columns",
   "id": "1d57d83bc17709fe"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-03T14:43:18.511039Z",
     "start_time": "2024-10-03T14:43:17.846171Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "\n",
    "air_quality = pd.read_csv(\"data/air_quality_no2.csv\", index_col=0, parse_dates=True)\n",
    "\n",
    "air_quality.head()"
   ],
   "id": "d9f39e380c161536",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                     station_antwerp  station_paris  station_london\n",
       "datetime                                                           \n",
       "2019-05-07 02:00:00              NaN            NaN            23.0\n",
       "2019-05-07 03:00:00             50.5           25.0            19.0\n",
       "2019-05-07 04:00:00             45.0           27.7            19.0\n",
       "2019-05-07 05:00:00              NaN           50.4            16.0\n",
       "2019-05-07 06:00:00              NaN           61.9             NaN"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station_antwerp</th>\n",
       "      <th>station_paris</th>\n",
       "      <th>station_london</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-07 02:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 03:00:00</th>\n",
       "      <td>50.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 04:00:00</th>\n",
       "      <td>45.0</td>\n",
       "      <td>27.7</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 05:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>50.4</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 06:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>61.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-03T14:44:19.521508Z",
     "start_time": "2024-10-03T14:44:19.516510Z"
    }
   },
   "cell_type": "code",
   "source": [
    "air_quality[\"london_mg_per_cubic\"] = air_quality[\"station_london\"] * 1.882\n",
    "\n",
    "air_quality.head()"
   ],
   "id": "3c31f2d40597eb03",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                     station_antwerp  station_paris  station_london  \\\n",
       "datetime                                                              \n",
       "2019-05-07 02:00:00              NaN            NaN            23.0   \n",
       "2019-05-07 03:00:00             50.5           25.0            19.0   \n",
       "2019-05-07 04:00:00             45.0           27.7            19.0   \n",
       "2019-05-07 05:00:00              NaN           50.4            16.0   \n",
       "2019-05-07 06:00:00              NaN           61.9             NaN   \n",
       "\n",
       "                     london_mg_per_cubic  \n",
       "datetime                                  \n",
       "2019-05-07 02:00:00               43.286  \n",
       "2019-05-07 03:00:00               35.758  \n",
       "2019-05-07 04:00:00               35.758  \n",
       "2019-05-07 05:00:00               30.112  \n",
       "2019-05-07 06:00:00                  NaN  "
      ],
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       "      <th>station_antwerp</th>\n",
       "      <th>station_paris</th>\n",
       "      <th>station_london</th>\n",
       "      <th>london_mg_per_cubic</th>\n",
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       "      <th>2019-05-07 02:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>43.286</td>\n",
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       "    <tr>\n",
       "      <th>2019-05-07 03:00:00</th>\n",
       "      <td>50.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 04:00:00</th>\n",
       "      <td>45.0</td>\n",
       "      <td>27.7</td>\n",
       "      <td>19.0</td>\n",
       "      <td>35.758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 05:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>50.4</td>\n",
       "      <td>16.0</td>\n",
       "      <td>30.112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 06:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>61.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-03T14:46:28.115514Z",
     "start_time": "2024-10-03T14:46:28.110986Z"
    }
   },
   "cell_type": "code",
   "source": [
    "air_quality[\"ratio_paris_antwerp\"] = (\n",
    "    air_quality[\"station_paris\"] / air_quality[\"station_antwerp\"]\n",
    ")\n",
    "\n",
    "\n",
    "air_quality.head()"
   ],
   "id": "3454e46f35b2c0f7",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                     station_antwerp  station_paris  station_london  \\\n",
       "datetime                                                              \n",
       "2019-05-07 02:00:00              NaN            NaN            23.0   \n",
       "2019-05-07 03:00:00             50.5           25.0            19.0   \n",
       "2019-05-07 04:00:00             45.0           27.7            19.0   \n",
       "2019-05-07 05:00:00              NaN           50.4            16.0   \n",
       "2019-05-07 06:00:00              NaN           61.9             NaN   \n",
       "\n",
       "                     london_mg_per_cubic  ratio_paris_antwerp  \n",
       "datetime                                                       \n",
       "2019-05-07 02:00:00               43.286                  NaN  \n",
       "2019-05-07 03:00:00               35.758             0.495050  \n",
       "2019-05-07 04:00:00               35.758             0.615556  \n",
       "2019-05-07 05:00:00               30.112                  NaN  \n",
       "2019-05-07 06:00:00                  NaN                  NaN  "
      ],
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       "      <th>station_antwerp</th>\n",
       "      <th>station_paris</th>\n",
       "      <th>station_london</th>\n",
       "      <th>london_mg_per_cubic</th>\n",
       "      <th>ratio_paris_antwerp</th>\n",
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       "    <tr>\n",
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       "      <th>2019-05-07 02:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>43.286</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2019-05-07 03:00:00</th>\n",
       "      <td>50.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>35.758</td>\n",
       "      <td>0.495050</td>\n",
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       "    <tr>\n",
       "      <th>2019-05-07 04:00:00</th>\n",
       "      <td>45.0</td>\n",
       "      <td>27.7</td>\n",
       "      <td>19.0</td>\n",
       "      <td>35.758</td>\n",
       "      <td>0.615556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 05:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>50.4</td>\n",
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       "      <td>30.112</td>\n",
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       "    <tr>\n",
       "      <th>2019-05-07 06:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>61.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# How to calculate summary statistics",
   "id": "826139abed62ead"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-03T14:49:38.143167Z",
     "start_time": "2024-10-03T14:49:38.134071Z"
    }
   },
   "cell_type": "code",
   "source": [
    "titanic = pd.read_csv(\"data/titanic.csv\")\n",
    "\n",
    "titanic.head()"
   ],
   "id": "7e529147e764e2eb",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ],
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-03T14:49:51.456485Z",
     "start_time": "2024-10-03T14:49:51.453177Z"
    }
   },
   "cell_type": "code",
   "source": "titanic[\"Age\"].mean()",
   "id": "681e9a9caed95e0e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-03T14:50:03.757305Z",
     "start_time": "2024-10-03T14:50:03.751639Z"
    }
   },
   "cell_type": "code",
   "source": "titanic[[\"Age\", \"Fare\"]].median()",
   "id": "78d1ef42dac3bc4a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age     28.0000\n",
       "Fare    14.4542\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-03T14:50:34.376219Z",
     "start_time": "2024-10-03T14:50:34.368006Z"
    }
   },
   "cell_type": "code",
   "source": "titanic[[\"Age\", \"Fare\"]].describe()",
   "id": "222ad2ac59f131d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "              Age        Fare\n",
       "count  714.000000  891.000000\n",
       "mean    29.699118   32.204208\n",
       "std     14.526497   49.693429\n",
       "min      0.420000    0.000000\n",
       "25%     20.125000    7.910400\n",
       "50%     28.000000   14.454200\n",
       "75%     38.000000   31.000000\n",
       "max     80.000000  512.329200"
      ],
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       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.699118</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.526497</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.125000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-03T14:50:50.257314Z",
     "start_time": "2024-10-03T14:50:50.251796Z"
    }
   },
   "cell_type": "code",
   "source": [
    "titanic.agg(\n",
    "    {\n",
    "        \"Age\": [\"min\", \"max\", \"median\", \"skew\"],\n",
    "        \"Fare\": [\"min\", \"max\", \"median\", \"mean\"],\n",
    "    }\n",
    ")\n"
   ],
   "id": "3544ba060cfe218",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "              Age        Fare\n",
       "min      0.420000    0.000000\n",
       "max     80.000000  512.329200\n",
       "median  28.000000   14.454200\n",
       "skew     0.389108         NaN\n",
       "mean          NaN   32.204208"
      ],
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skew</th>\n",
       "      <td>0.389108</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>32.204208</td>\n",
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       "</table>\n",
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      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-03T14:52:12.895634Z",
     "start_time": "2024-10-03T14:52:12.889136Z"
    }
   },
   "cell_type": "code",
   "source": "titanic[[\"Sex\", \"Age\"]].groupby(\"Sex\").mean()",
   "id": "9fb03c9ed27aa6ce",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "              Age\n",
       "Sex              \n",
       "female  27.915709\n",
       "male    30.726645"
      ],
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       "      <th>Age</th>\n",
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       "      <th>Sex</th>\n",
       "      <th></th>\n",
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       "      <th>female</th>\n",
       "      <td>27.915709</td>\n",
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       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>30.726645</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# How to reshape the layout of tables",
   "id": "8a6d1cb1a4b7e2fd"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-03T14:57:25.084484Z",
     "start_time": "2024-10-03T14:57:25.078783Z"
    }
   },
   "cell_type": "code",
   "source": "titanic.sort_values(by=\"Age\").head()",
   "id": "252891d7b56f637f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     PassengerId  Survived  Pclass                             Name     Sex  \\\n",
       "803          804         1       3  Thomas, Master. Assad Alexander    male   \n",
       "755          756         1       2        Hamalainen, Master. Viljo    male   \n",
       "644          645         1       3           Baclini, Miss. Eugenie  female   \n",
       "469          470         1       3    Baclini, Miss. Helene Barbara  female   \n",
       "78            79         1       2    Caldwell, Master. Alden Gates    male   \n",
       "\n",
       "      Age  SibSp  Parch  Ticket     Fare Cabin Embarked  \n",
       "803  0.42      0      1    2625   8.5167   NaN        C  \n",
       "755  0.67      1      1  250649  14.5000   NaN        S  \n",
       "644  0.75      2      1    2666  19.2583   NaN        C  \n",
       "469  0.75      2      1    2666  19.2583   NaN        C  \n",
       "78   0.83      0      2  248738  29.0000   NaN        S  "
      ],
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       "      <th>Survived</th>\n",
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       "      <th>803</th>\n",
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       "      <td>3</td>\n",
       "      <td>Thomas, Master. Assad Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2625</td>\n",
       "      <td>8.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
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       "      <th>755</th>\n",
       "      <td>756</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hamalainen, Master. Viljo</td>\n",
       "      <td>male</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>250649</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>645</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Baclini, Miss. Eugenie</td>\n",
       "      <td>female</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2666</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>470</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Baclini, Miss. Helene Barbara</td>\n",
       "      <td>female</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2666</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
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       "      <th>78</th>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Caldwell, Master. Alden Gates</td>\n",
       "      <td>male</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>248738</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-03T15:22:30.405543Z",
     "start_time": "2024-10-03T15:22:30.377512Z"
    }
   },
   "cell_type": "code",
   "source": [
    "air_quality = pd.read_csv(\n",
    "    \"data/air_quality_long.csv\", index_col=\"date.utc\", parse_dates=True\n",
    ")\n",
    "\n",
    "air_quality.pivot_table(\n",
    "    values=\"value\", index=\"location\", columns=\"parameter\", aggfunc=\"mean\"\n",
    ")"
   ],
   "id": "dfb663b7b77ed191",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "parameter                 no2       pm25\n",
       "location                                \n",
       "BETR801             26.950920  23.169492\n",
       "FR04014             29.374284        NaN\n",
       "London Westminster  29.740050  13.443568"
      ],
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       "      <th>parameter</th>\n",
       "      <th>no2</th>\n",
       "      <th>pm25</th>\n",
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       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BETR801</th>\n",
       "      <td>26.950920</td>\n",
       "      <td>23.169492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR04014</th>\n",
       "      <td>29.374284</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London Westminster</th>\n",
       "      <td>29.740050</td>\n",
       "      <td>13.443568</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# How to combine data from multiple tables",
   "id": "c5a0abc98b3b06db"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-03T15:27:57.180791Z",
     "start_time": "2024-10-03T15:27:57.172309Z"
    }
   },
   "cell_type": "code",
   "source": [
    "air_quality_no2 = pd.read_csv(\"data/air_quality_no2_long.csv\",\n",
    "                              parse_dates=True)\n",
    "\n",
    "\n",
    "air_quality_no2 = air_quality_no2[[\"date.utc\", \"location\",\n",
    "                                   \"parameter\", \"value\"]]\n",
    "\n",
    "\n",
    "air_quality_no2.head()"
   ],
   "id": "c04b376e4ce7dd90",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                    date.utc location parameter  value\n",
       "0  2019-06-21 00:00:00+00:00  FR04014       no2   20.0\n",
       "1  2019-06-20 23:00:00+00:00  FR04014       no2   21.8\n",
       "2  2019-06-20 22:00:00+00:00  FR04014       no2   26.5\n",
       "3  2019-06-20 21:00:00+00:00  FR04014       no2   24.9\n",
       "4  2019-06-20 20:00:00+00:00  FR04014       no2   21.4"
      ],
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       "      <th>value</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>2019-06-21 00:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-06-20 23:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>21.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-06-20 22:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>26.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-06-20 21:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>24.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-06-20 20:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>21.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-03T15:28:18.765741Z",
     "start_time": "2024-10-03T15:28:18.759109Z"
    }
   },
   "cell_type": "code",
   "source": [
    "air_quality_pm25 = pd.read_csv(\"data/air_quality_pm25_long.csv\",\n",
    "                               parse_dates=True)\n",
    "\n",
    "\n",
    "air_quality_pm25 = air_quality_pm25[[\"date.utc\", \"location\",\n",
    "                                     \"parameter\", \"value\"]]\n",
    "\n",
    "\n",
    "air_quality_pm25.head()"
   ],
   "id": "1104f00ff13bcc2e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                    date.utc location parameter  value\n",
       "0  2019-06-18 06:00:00+00:00  BETR801      pm25   18.0\n",
       "1  2019-06-17 08:00:00+00:00  BETR801      pm25    6.5\n",
       "2  2019-06-17 07:00:00+00:00  BETR801      pm25   18.5\n",
       "3  2019-06-17 06:00:00+00:00  BETR801      pm25   16.0\n",
       "4  2019-06-17 05:00:00+00:00  BETR801      pm25    7.5"
      ],
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       "      <th></th>\n",
       "      <th>date.utc</th>\n",
       "      <th>location</th>\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>2019-06-18 06:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>18.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-06-17 08:00:00+00:00</td>\n",
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       "      <td>6.5</td>\n",
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       "      <td>2019-06-17 07:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-06-17 06:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-06-17 05:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-03T15:29:02.858865Z",
     "start_time": "2024-10-03T15:29:02.853416Z"
    }
   },
   "cell_type": "code",
   "source": [
    "air_quality = pd.concat([air_quality_pm25, air_quality_no2], axis=0)\n",
    "\n",
    "air_quality.head()"
   ],
   "id": "9a161ef9ddd84be3",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                    date.utc location parameter  value\n",
       "0  2019-06-18 06:00:00+00:00  BETR801      pm25   18.0\n",
       "1  2019-06-17 08:00:00+00:00  BETR801      pm25    6.5\n",
       "2  2019-06-17 07:00:00+00:00  BETR801      pm25   18.5\n",
       "3  2019-06-17 06:00:00+00:00  BETR801      pm25   16.0\n",
       "4  2019-06-17 05:00:00+00:00  BETR801      pm25    7.5"
      ],
      "text/html": [
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       "      <th>date.utc</th>\n",
       "      <th>location</th>\n",
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       "      <th>value</th>\n",
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       "      <th>0</th>\n",
       "      <td>2019-06-18 06:00:00+00:00</td>\n",
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       "      <td>18.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-06-17 08:00:00+00:00</td>\n",
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       "      <td>6.5</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-06-17 07:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-06-17 06:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-06-17 05:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# How to handle time series data with ease",
   "id": "a063af43378bb35d"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-05T14:41:18.610639Z",
     "start_time": "2024-10-05T14:41:16.721007Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "air_quality = pd.read_csv(\"data/air_quality_no2_long.csv\")\n",
    "\n",
    "air_quality = air_quality.rename(columns={\"date.utc\": \"datetime\"})\n",
    "\n",
    "air_quality.head()"
   ],
   "id": "a0be99c330dc08ab",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    city country                   datetime location parameter  value   unit\n",
       "0  Paris      FR  2019-06-21 00:00:00+00:00  FR04014       no2   20.0  µg/m³\n",
       "1  Paris      FR  2019-06-20 23:00:00+00:00  FR04014       no2   21.8  µg/m³\n",
       "2  Paris      FR  2019-06-20 22:00:00+00:00  FR04014       no2   26.5  µg/m³\n",
       "3  Paris      FR  2019-06-20 21:00:00+00:00  FR04014       no2   24.9  µg/m³\n",
       "4  Paris      FR  2019-06-20 20:00:00+00:00  FR04014       no2   21.4  µg/m³"
      ],
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>datetime</th>\n",
       "      <th>location</th>\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-21 00:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 23:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>21.8</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 22:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>26.5</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 21:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>24.9</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 20:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>21.4</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-05T14:42:36.109113Z",
     "start_time": "2024-10-05T14:42:36.103467Z"
    }
   },
   "cell_type": "code",
   "source": [
    "air_quality[\"datetime\"] = pd.to_datetime(air_quality[\"datetime\"])\n",
    "\n",
    "air_quality[\"datetime\"]"
   ],
   "id": "9d7f360e5dfccf88",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2019-06-21 00:00:00+00:00\n",
       "1      2019-06-20 23:00:00+00:00\n",
       "2      2019-06-20 22:00:00+00:00\n",
       "3      2019-06-20 21:00:00+00:00\n",
       "4      2019-06-20 20:00:00+00:00\n",
       "                  ...           \n",
       "2063   2019-05-07 06:00:00+00:00\n",
       "2064   2019-05-07 04:00:00+00:00\n",
       "2065   2019-05-07 03:00:00+00:00\n",
       "2066   2019-05-07 02:00:00+00:00\n",
       "2067   2019-05-07 01:00:00+00:00\n",
       "Name: datetime, Length: 2068, dtype: datetime64[ns, UTC]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-05T14:44:39.120717Z",
     "start_time": "2024-10-05T14:44:39.116093Z"
    }
   },
   "cell_type": "code",
   "source": [
    "air_quality[\"month\"] = air_quality[\"datetime\"].dt.month\n",
    "\n",
    "air_quality.head()"
   ],
   "id": "e509cbfe18454d0b",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    city country                  datetime location parameter  value   unit  \\\n",
       "0  Paris      FR 2019-06-21 00:00:00+00:00  FR04014       no2   20.0  µg/m³   \n",
       "1  Paris      FR 2019-06-20 23:00:00+00:00  FR04014       no2   21.8  µg/m³   \n",
       "2  Paris      FR 2019-06-20 22:00:00+00:00  FR04014       no2   26.5  µg/m³   \n",
       "3  Paris      FR 2019-06-20 21:00:00+00:00  FR04014       no2   24.9  µg/m³   \n",
       "4  Paris      FR 2019-06-20 20:00:00+00:00  FR04014       no2   21.4  µg/m³   \n",
       "\n",
       "   month  \n",
       "0      6  \n",
       "1      6  \n",
       "2      6  \n",
       "3      6  \n",
       "4      6  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>datetime</th>\n",
       "      <th>location</th>\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "      <th>unit</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-21 00:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>µg/m³</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 23:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>21.8</td>\n",
       "      <td>µg/m³</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 22:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>26.5</td>\n",
       "      <td>µg/m³</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 21:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>24.9</td>\n",
       "      <td>µg/m³</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 20:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>21.4</td>\n",
       "      <td>µg/m³</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-05T14:47:15.898290Z",
     "start_time": "2024-10-05T14:47:15.821301Z"
    }
   },
   "cell_type": "code",
   "source": [
    "fig, axs = plt.subplots(figsize=(12, 4))\n",
    "\n",
    "air_quality.groupby(air_quality[\"datetime\"].dt.hour)[\"value\"].mean().plot(\n",
    "    kind='bar', rot=0, ax=axs\n",
    ")\n",
    "plt.xlabel(\"Hour of the day\");  # custom x label using Matplotlib\n",
    "\n",
    "plt.ylabel(\"$NO_2 (µg/m^3)$\");"
   ],
   "id": "6cb65f7632d86ca1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-05T14:47:02.097489Z",
     "start_time": "2024-10-05T14:47:02.008188Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.xlabel(\"Hour of the day\");  # custom x label using Matplotlib\n",
    "\n",
    "plt.ylabel(\"$NO_2 (µg/m^3)$\");"
   ],
   "id": "cb8a98203fbd1b03",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# How to manipulate textual data",
   "id": "bfeba3aeecf40540"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-05T14:50:13.639742Z",
     "start_time": "2024-10-05T14:50:13.633018Z"
    }
   },
   "cell_type": "code",
   "source": [
    "titanic = pd.read_csv(\"data/titanic.csv\")\n",
    "\n",
    "titanic.head()"
   ],
   "id": "d6095fd692c850b7",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
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       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
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      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-05T14:50:29.826964Z",
     "start_time": "2024-10-05T14:50:29.822979Z"
    }
   },
   "cell_type": "code",
   "source": "titanic[\"Name\"].str.lower()",
   "id": "9a3b060075754b07",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                braund, mr. owen harris\n",
       "1      cumings, mrs. john bradley (florence briggs th...\n",
       "2                                 heikkinen, miss. laina\n",
       "3           futrelle, mrs. jacques heath (lily may peel)\n",
       "4                               allen, mr. william henry\n",
       "                             ...                        \n",
       "886                                montvila, rev. juozas\n",
       "887                         graham, miss. margaret edith\n",
       "888             johnston, miss. catherine helen \"carrie\"\n",
       "889                                behr, mr. karl howell\n",
       "890                                  dooley, mr. patrick\n",
       "Name: Name, Length: 891, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-05T14:51:59.739559Z",
     "start_time": "2024-10-05T14:51:59.735197Z"
    }
   },
   "cell_type": "code",
   "source": [
    "titanic[\"Surname\"] = titanic[\"Name\"].str.split(\",\").str.get(0)\n",
    "\n",
    "titanic[\"Surname\"]"
   ],
   "id": "be9bf5ce82917857",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Braund\n",
       "1        Cumings\n",
       "2      Heikkinen\n",
       "3       Futrelle\n",
       "4          Allen\n",
       "         ...    \n",
       "886     Montvila\n",
       "887       Graham\n",
       "888     Johnston\n",
       "889         Behr\n",
       "890       Dooley\n",
       "Name: Surname, Length: 891, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 9
  }
 ],
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